scplotter to work with 10x Visium data prepared by Seurat¶
Go back to scplotter documentation: https://pwwang.github.io/scplotter/
Mouse Brain: 10x Genomics Xenium In Situ¶
In [1]:
suppressPackageStartupMessages({
library(Seurat)
})
# Load the scplotter package
# library(scplotter)
devtools::load_all()
# devtools::load_all("../../../plotthis")
path <- "data/xenium_tiny_subset/outs"
# path <- "data/Xenium_Giotto_workshop"
# Load the Xenium data
xenium.obj <- LoadXenium(path, fov = "fov")
# remove cells with 0 counts
xenium.obj <- subset(xenium.obj, subset = nCount_Xenium > 0)
xenium.obj
ℹ Loading scplotter Warning message: “cells did not contain a segmentation_method column. Skipping...” Genome matrix has multiple modalities, returning a list of matrices for this genome Warning message: “Feature names cannot have underscores ('_'), replacing with dashes ('-')” Warning message: “Feature names cannot have underscores ('_'), replacing with dashes ('-')” Warning message: “Feature names cannot have underscores ('_'), replacing with dashes ('-')” Warning message: “Feature names cannot have underscores ('_'), replacing with dashes ('-')” Warning message: “Feature names cannot have underscores ('_'), replacing with dashes ('-')” Warning message: “Feature names cannot have underscores ('_'), replacing with dashes ('-')” Warning message: “Not validating FOV objects” Warning message: “Not validating Centroids objects” Warning message: “Not validating Centroids objects” Warning message: “Not validating FOV objects” Warning message: “Not validating FOV objects” Warning message: “Not validating FOV objects” Warning message: “Not validating Seurat objects”
An object of class Seurat 541 features across 36553 samples within 4 assays Active assay: Xenium (248 features, 0 variable features) 1 layer present: counts 3 other assays present: BlankCodeword, ControlCodeword, ControlProbe 1 spatial field of view present: fov
In [2]:
options(repr.plot.width = 10, repr.plot.height = 5)
FeatureStatPlot(xenium.obj, features = c("nFeature_Xenium", "nCount_Xenium"),
facet_scales = "free_y")
Warning message in GetAssayData.StdAssay(object = object[[assay]], layer = layer): “data layer is not found and counts layer is used”
In [3]:
options(repr.plot.width = 6, repr.plot.height = 8)
# devtools::load_all()
SpatDimPlot(xenium.obj, image = "black", features = c("Gad1", "Sst", "Pvalb", "Gfap"),
nmols = 20000, points_size = 0.1, points_palette = "Set1")
In [4]:
options(repr.plot.width = 8, repr.plot.height = 12)
SpatFeaturePlot(xenium.obj, layer = "counts", image = "black",
features = c("Cux2", "Rorb", "Bcl11b", "Foxp2"),
points_size = 0.2, points_color_name = "Expression")
In [6]:
options(repr.plot.width = 11, repr.plot.height = 5, future.globals.maxSize = 1024 ^ 3)
cropped.coords <- Crop(xenium.obj[["fov"]], x = c(1200, 2900), y = c(3750, 4550), coords = "plot")
xenium.obj[["zoom"]] <- cropped.coords
# visualize cropped area with cell segmentations & selected molecules
# The segmentation boundary was not loaded anyway...
# DefaultBoundary(xenium.obj[["zoom"]]) <- "segmentation"
SpatDimPlot(xenium.obj, fov = "zoom", image = "black", features = c("Gad1", "Sst", "Npy2r", "Pvalb", "Nrn1"),
nmols = 10000, points_size = 0.1, points_palette = "Set1", shapes = TRUE)
Warning message: “Key ‘Xenium_’ taken, using ‘zoom_’ instead” Warning message in SpatPlot.Seurat.FOV(object, image = image, ...): “[SpatPlot] 'shapes' is set to TRUE, meaning the same boundaries as points will be used. You may want to provide a different boundaries for shapes. Otherwise the shapes is plotted as points.”
In [7]:
xenium.obj <- SCTransform(xenium.obj, assay = "Xenium")
xenium.obj <- RunPCA(xenium.obj, npcs = 30, features = rownames(xenium.obj))
xenium.obj <- RunUMAP(xenium.obj, dims = 1:30)
xenium.obj <- FindNeighbors(xenium.obj, reduction = "pca", dims = 1:30)
xenium.obj <- FindClusters(xenium.obj, resolution = 0.3)
Running SCTransform on assay: Xenium vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes. Calculating cell attributes from input UMI matrix: log_umi Variance stabilizing transformation of count matrix of size 248 by 36553 Model formula is y ~ log_umi Get Negative Binomial regression parameters per gene Using 248 genes, 5000 cells Second step: Get residuals using fitted parameters for 248 genes Computing corrected count matrix for 248 genes Calculating gene attributes Wall clock passed: Time difference of 3.678288 secs Determine variable features Centering data matrix Place corrected count matrix in counts slot Set default assay to SCT PC_ 1 Positive: Slc17a7, Nrn1, Epha4, Neurod6, Nwd2, Gad1, Cpne4, Rasgrf2, Rims3, Lamp5 2010300C02Rik, Dkk3, Slc17a6, Pvalb, Garnl3, Cpne6, Fhod3, Plcxd2, Gad2, Tmem132d Kcnh5, Dner, Calb1, Bhlhe22, Bcl11b, Nell1, Bdnf, Rasl10a, Satb2, Arc Negative: Igf2, Dcn, Fmod, Slc13a4, Fn1, Aldh1a2, Col1a1, Ly6a, Cldn5, Spp1 Gfap, Nr2f2, Gjb2, Cyp1b1, Acta2, Pecam1, Adgrl4, Pdgfra, Acvrl1, Kdr Cd93, Ccn2, Cobll1, Fgd5, Sox17, Igfbp5, Carmn, Lyz2, Pglyrp1, Emcn PC_ 2 Positive: Gjc3, Opalin, Sox10, Gfap, Clmn, Vwc2l, Zfp536, Sema6a, Gpr17, Gng12 Tmem163, Prox1, Adamtsl1, Dpy19l1, Cobll1, Cdh20, Arhgef28, Igfbp5, Chrm2, Sema3d Carmn, Aqp4, Fign, Pdgfra, Cspg4, Ntsr2, Lyz2, Siglech, Adamts2, Rmst Negative: Slc17a7, Fn1, Igf2, Nrn1, Cldn5, Epha4, Neurod6, Ly6a, Dcn, Rasgrf2 Lamp5, Fmod, Aldh1a2, Dkk3, 2010300C02Rik, Slc13a4, Car4, Nwd2, Pecam1, Gad1 Col1a1, Igfbp4, Spp1, Cpne4, Rims3, Igfbp6, Acvrl1, Cpne6, Adgrl4, Calb1 PC_ 3 Positive: Cldn5, Ly6a, Adgrl4, Fn1, Pecam1, Acvrl1, Kdr, Cd93, Pglyrp1, Sox17 Emcn, Car4, Nostrin, Fgd5, Zfp366, Mecom, Slfn5, Paqr5, Arc, Cabp7 Cobll1, Laptm5, Acsbg1, Gjc3, Siglech, Opalin, Kctd12, Ntsr2, Trem2, Sema6a Negative: Slc13a4, Igf2, Dcn, Fmod, Aldh1a2, Nwd2, Col1a1, Vat1l, Calb2, Spp1 Pdgfra, Gjb2, Necab2, Slc17a6, Cyp1b1, Syt6, Nr2f2, Nrp2, Dner, Slit2 Col6a1, Cpne4, Spag16, Strip2, Sncg, Thsd7a, Ppp1r1b, Gucy1a1, Mapk4, Chat PC_ 4 Positive: Slc17a7, Dkk3, Cabp7, Neurod6, 2010300C02Rik, Arc, Epha4, Igfbp4, Bcl11b, Fmod Meis2, Laptm5, Dcn, Gad1, Bhlhe22, Aldh1a2, Cpne6, Rasl10a, Lamp5, Col1a1 Col6a1, Igfbp6, Gfap, Satb2, Trem2, Cplx3, Gm2115, Gfra2, Garnl3, Siglech Negative: Nwd2, Calb2, Slc17a6, Necab2, Syt6, Vat1l, Nrp2, Sncg, Cpne4, Cldn5 Ly6a, Dner, Gucy1a1, Thsd7a, Chat, Kctd8, Tmem163, Tacr1, Tmem255a, Cacna2d2 Adgrl4, Pecam1, Cntnap4, Rmst, Cbln1, Fn1, Kdr, Cd93, Inpp4b, Acvrl1 PC_ 5 Positive: Gad1, Pvalb, Gad2, Rab3b, Opalin, Gjc3, Dpy19l1, Cdh13, Sox10, Garnl3 Parm1, Rims3, Tmem132d, Lamp5, Neto2, Vip, Btbd11, Cntnap4, Plcxd2, Ccn2 Cort, Penk, Fn1, Fhod3, Zfp536, Spp1, Col6a1, Nxph3, Sst, Dcn Negative: Cabp7, Gfap, Aqp4, Laptm5, Ntsr2, Trem2, Siglech, Acsbg1, Cd53, Slc39a12 2010300C02Rik, Kctd12, Cpne4, Cd300c2, Bhlhe22, Gm2115, Ikzf1, Rfx4, Necab2, Igfbp5 Sipa1l3, Cpne6, Cd68, Clmn, Rmst, Nwd2, Nrp2, Orai2, Spi1, Prdm8 Warning message: “The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation' This message will be shown once per session” 12:04:15 UMAP embedding parameters a = 0.9922 b = 1.112 Found more than one class "dist" in cache; using the first, from namespace 'spam' Also defined by ‘BiocGenerics’ 12:04:15 Read 36553 rows and found 30 numeric columns 12:04:15 Using Annoy for neighbor search, n_neighbors = 30 Found more than one class "dist" in cache; using the first, from namespace 'spam' Also defined by ‘BiocGenerics’ 12:04:15 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * | 12:04:19 Writing NN index file to temp file /tmp/m161047/RtmpJk6gFt/file676414025e0c9 12:04:19 Searching Annoy index using 1 thread, search_k = 3000 12:04:30 Annoy recall = 100% 12:04:31 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30 12:04:33 Initializing from normalized Laplacian + noise (using RSpectra) 12:04:34 Commencing optimization for 200 epochs, with 1669008 positive edges 12:04:52 Optimization finished Computing nearest neighbor graph Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck Number of nodes: 36553 Number of edges: 1340890 Running Louvain algorithm... Maximum modularity in 10 random starts: 0.9587 Number of communities: 28 Elapsed time: 4 seconds
In [8]:
options(repr.plot.width = 6, repr.plot.height = 8)
SpatDimPlot(xenium.obj, image = "black", points_size = 0.1)
Warning message: “No FOV associated with assay 'SCT', using global default FOV”
In [10]:
options(repr.plot.width = 7, repr.plot.height = 8)
SpatFeaturePlot(xenium.obj, layer = "counts", image = "black",
features = "Slc17a7", points_size = 0.2, points_color_name = "Slc17a7 Expression")
Warning message: “No FOV associated with assay 'SCT', using global default FOV”
In [ ]:
options(repr.plot.width = 8, repr.plot.height = 7)
crop <- Crop(xenium.obj[["fov"]], x = c(600, 2100), y = c(900, 4700))
xenium.obj[["crop"]] <- crop
p1 <- SpatFeaturePlot(xenium.obj, fov = "crop", features = "Slc17a7",
image = "black", points_size = 0.2)
# use ext argument to crop
p2 <- SpatFeaturePlot(xenium.obj, ext = c(600, 2100, 900, 4700), features = "Slc17a7",
image = "black", points_size = 0.2)
p1 + p2
Warning message: “Key ‘Xenium_’ taken, using ‘crop_’ instead” Warning message in `[<-.data.frame`(`*tmp*`, , features, value = structure(list(: “replacement element 1 has 36553 rows to replace 11872 rows” Warning message: “No FOV associated with assay 'SCT', using global default FOV”
Mini Xenium Dataset provided by Giotto vignette¶
See: https://drieslab.github.io/giotto_workshop_2024/xenium-1.html
In [22]:
path <- "data/Xenium_Giotto_workshop"
# Load the Xenium data
g <- LoadXenium(path, fov = "fov")
# remove cells with 0 counts
g <- subset(g, subset = nCount_Xenium > 0)
g
Warning message: “cells did not contain a segmentation_method column. Skipping...”
Error in option$fn(file.path(data.dir, option$filename)) : File not found
10X data contains more than one type and is being returned as a list containing matrices of each type.
Warning message:
“Feature names cannot have underscores ('_'), replacing with dashes ('-')”
Warning message:
“Feature names cannot have underscores ('_'), replacing with dashes ('-')”
Warning message:
“Feature names cannot have underscores ('_'), replacing with dashes ('-')”
Warning message:
“Feature names cannot have underscores ('_'), replacing with dashes ('-')”
Warning message:
“Feature names cannot have underscores ('_'), replacing with dashes ('-')”
Warning message:
“Feature names cannot have underscores ('_'), replacing with dashes ('-')”
Warning message:
“Not validating FOV objects”
Warning message:
“Not validating Centroids objects”
Warning message:
“Not validating Centroids objects”
Warning message:
“Not validating FOV objects”
Warning message:
“Not validating FOV objects”
Warning message:
“Not validating FOV objects”
Warning message:
“Not validating Seurat objects”
An object of class Seurat 541 features across 7654 samples within 4 assays Active assay: Xenium (377 features, 0 variable features) 1 layer present: counts 3 other assays present: BlankCodeword, ControlCodeword, ControlProbe 1 spatial field of view present: fov
In [34]:
# Simple Visualization
options(repr.plot.width = 7, repr.plot.height = 6)
SpatDimPlot(
g,
image = "black",
# put shapes at last
layers = c("image", "points", "shapes"),
features = c("ABCC11", "ACE2", "ACKR1", "ACTA2", "ACTG2", "ADAM28"),
shapes_border_color = "cyan",
shapes_border_size = 0.1,
shapes_fill_by = "black",
points_size = 0.1,
nmols = 10000
)